RVC_PCA_analysis
Stephanie Goya
2023-12-18
Upload the dataframes and alignment (in FASTA) to calculate the pairwise genetic distance matrix:
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## as.alignment, consensus## Loading required package: ggplot2## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa## corrplot 0.92 loadedCalculate the eigenvalues and principal component analysis:
Analysis of quality and contribution of individuals:
Evaluating the PCA by clustering based on metadata information:
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Constructing a dendrogram and making the hierarchical clustering:
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## cutreelibrary("svglite")
RVCdm<-dist(RVCdistdf, method = 'euclidean')
RVChc<-hclust(RVCdm, method="complete") # simple dendrogram
#evaluating the number of clusters con average silhouette:
fviz_nbclust(RVCdistdf, FUNcluster=hcut, method="silhouette", k.max = 10)
fviz_nbclust(RVCdistdf, FUNcluster=hcut, method="wss")
cut_cmp <- cutree(RVChc, k = 7) #applying the clustering by wss
plot(RVChc, hang=-1, cex=0.2)
#plot with cluster rectangles within the clusters:
rect.hclust(RVChc, k=7, border=2:7)
#save plot of dendrogram:
#svg(filename = "RVC_HC_PCA_euclidean.svg")
#plot(RVChc, hang=-1, cex=0.2)
#rect.hclust(RVChc, k=7, border=2:7) #if I want to see the clusters identified by a rectangle
#dev.off()Evaluate the association of metadata with the clusters by hierarchical clustering:
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## 1 age 1 2 22 34 366. 0.893 1 ns
## 2 age 1 3 22 20 114. 0.008 0.171 ns
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## 4 age 1 5 22 37 300. 0.096 1 ns
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## 10 age 2 6 34 38 653 0.941 1 ns
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